Zobrazeno 1 - 10
of 58
pro vyhledávání: '"De Stefani, Lorenzo"'
We introduce the notion of an $r$-visit of a Directed Acyclic Graph DAG $G=(V,E)$, a sequence of the vertices of the DAG complying with a given rule $r$. A rule $r$ specifies for each vertex $v\in V$ a family of $r$-enabling sets of (immediate) prede
Externí odkaz:
http://arxiv.org/abs/2210.01897
Autor:
De Stefani, Lorenzo
We present COPSIM a parallel implementation of standard integer multiplication for the distributed memory setting, and COPK a parallel implementation of Karatsuba's fast integer multiplication algorithm for a distributed memory setting. When using $\
Externí odkaz:
http://arxiv.org/abs/2009.14590
Autor:
De Stefani, Lorenzo
Almost asymptotically tight lower bounds are derived for the Input/Output (I/O) complexity $IO_\mathcal{A}\left(n,M\right)$ of a general class of hybrid algorithms computing the product of two integers, each represented with $n$ digits in a given bas
Externí odkaz:
http://arxiv.org/abs/1912.08045
Autor:
De Stefani, Lorenzo, Upfal, Eli
While standard statistical inference techniques and machine learning generalization bounds assume that tests are run on data selected independently of the hypotheses, practical data analysis and machine learning are usually iterative and adaptive pro
Externí odkaz:
http://arxiv.org/abs/1910.03493
Autor:
De Stefani, Lorenzo
Asymptotically tight lower bounds are derived for the I/O complexity of a general class of hybrid algorithms computing the product of $n \times n$ square matrices combining ``\emph{Strassen-like}'' fast matrix multiplication approach with computation
Externí odkaz:
http://arxiv.org/abs/1904.12804
Visual representations of data (visualizations) are tools of great importance and widespread use in data analytics as they provide users visual insight to patterns in the observed data in a simple and effective way. However, since visualizations tool
Externí odkaz:
http://arxiv.org/abs/1811.00602
Tiered Sampling: An Efficient Method for Approximate Counting Sparse Motifs in Massive Graph Streams
We introduce Tiered Sampling, a novel technique for approximate counting sparse motifs in massive graphs whose edges are observed in a stream. Our technique requires only a single pass on the data and uses a memory of fixed size $M$, which can be mag
Externí odkaz:
http://arxiv.org/abs/1710.02108
Autor:
Zhao, Zheguang, De Stefani, Lorenzo, Zgraggen, Emanuel, Binnig, Carsten, Upfal, Eli, Kraska, Tim
Recent tools for interactive data exploration significantly increase the chance that users make false discoveries. The crux is that these tools implicitly allow the user to test a large body of different hypotheses with just a few clicks thus incurri
Externí odkaz:
http://arxiv.org/abs/1612.01040
A tight $\Omega((n/\sqrt{M})^{\log_2 7}M)$ lower bound is derived on the \io complexity of Strassen's algorithm to multiply two $n \times n$ matrices, in a two-level storage hierarchy with $M$ words of fast memory. A proof technique is introduced, wh
Externí odkaz:
http://arxiv.org/abs/1605.02224
We present TRI\`EST, a suite of one-pass streaming algorithms to compute unbiased, low-variance, high-quality approximations of the global and local (i.e., incident to each vertex) number of triangles in a fully-dynamic graph represented as an advers
Externí odkaz:
http://arxiv.org/abs/1602.07424